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Posterior reversible encephalopathy syndrome (PRES) can present with focal neurologic deficits, mimicking a stroke and can often represent a diagnostic challenge when presenting atypically. A high degree of suspicion is required in the clinical setting in order to yield the diagnosis. Cerebral CT perfusion (CTP) is utilized in many institutions as the first line in acute stroke imaging. CTP has proved to be a very sensitive measure of cerebral blood flow dynamics, most commonly employed to delineate the infarcted tissue from penumbra (at-risk tissue) in ischemic strokes. But abnormal CTP is also seen in stroke mimics such as seizures, hypoglycemia, tumors, migraines and PRES. In this article we describe a case of PRES in an elderly bone marrow transplant recipient who presented with focal neurological deficits concerning for a cerebrovascular accident. CTP played a pivotal role in the diagnosis and initiation of appropriate management. We also briefly discuss the pathophysiology of PRES.

In this issue’s Clinical Research Capsule, we ask the fundamental question: “Why care about research?” Clinicians sometimes have a distanced attitude towards research. The reasons for this are many: conducting research is difficult; it is only for those who are statistically minded; it means having NIH or equivalent funding; finally, it does not pertain to them as research is separate from patient care. The notion that research is separate and without impact on patient care could not be more erroneous. Why care about clinical research?  Our practice is our laboratory  Some nuances of human disease not translatable from animal models (social admit reasons, “look sicker”, )  Clinical research allows us to continually refine patient care Let’s look at a few examples: You are evaluating a patient with an acute ankle injury. Do you recognize the following questions?

Emergency department (ED) over-crowding has been a topic of intense interest over the past few years, with the Institute of Medicine report “Emergency Care at a Crossroads” published in 2006 [1], followed by the American College of Emergency Physicians task force report on boarding (2008) and, most recently, the United States Government Accountability Office (GAO) Report to the Chairman [2], Committee on Finance, U.S. Senate published in April 2009. Similarly, the Canadian Association of Emergency Physicians and the Australasian College for Emergency Medicine have also recognized the primary problem of over-crowding in the ED [3, 4]. The adverse effects of the global problem of ED over-crowding are well known. Crowding negatively impacts all stakeholders: patients, physicians, and the hospital. Most important is patient safety, with decreased quality of care and an increase in medical errors in overcrowded EDs. Further, patients have a poor experience, which leads them to leave without being seen, and they are less likely to return to the ED in the future. Physicians and other providers experience decreased job satisfaction, resulting in decreased productivity and increased staff turnover. For the hospital, ED over-crowding results in lost revenue from multiple sources. Revenue is lost from patients who leave without being seen, from emergency medical service (EMS) diversion secondary to dissatisfaction among both patients and EMS crews, and from shifting of the market share to competitors. Consider the real mortality risk associated with ED over-crowding. A study of over 55,000 ED patients demonstrated a higher mortality rate among patients boarded in the ED than in those moved to an inpatient hallway bed (p 155,000 patient visits per year) tertiary referral facility with 1,400 beds which serves as the primary teaching hospital of the Chinese University of Hong Kong. The authors note that while the SARS epidemic and the introduction of co-pay in the ED resulted in an overall net decrease of ED volumes, the concurrent increase in patients brought in via ambulance plus reduction of medical and nursing staff resulted in increased waiting times. The over-crowding problem is compounded by the marked increase in the medical admission rate, which correlates with the 12% increase in the geriatric population visits. The second study, by Kulstad and Kelley [7], entitled “Over-crowding is associated with delays in percutaneous coronary intervention for acute myocardial infarction” uses the EDWIN score as a marker of ED over-crowding and its association with ultimate delays in patients receiving percutaneous intervention for acute myocardial infarction. The EDWIN score is the sum of the number of patients in the ED multiplied by their acuity, divided by the number of attending physicians working in the ED, multiplied by the number of inpatient boarders. The authors report a decreased likelihood of timely treatment for acute MI during times of over-crowding in their emergency department. These studies underscore the very real and detrimental effects of ED over-crowding. The most important root cause of ED over-crowding is the inability to transfer emergency patients to inpatient beds and the resultant boarding of admitted patients in the ED. Solutions to decrease boarding in the ED include moving boarders into inpatient hallways at the front end, and increasing the rate of discharges by noon at the back end. Movement of patients to inpatient hallways, while known to be safer for patients, still often results in push-back from the inpatient services. One commonly cited reason is patient dissatisfaction. As the science of ED over-crowding evolves, studies addressing this very issue have also been performed. A study of 445 patients reports an overall preference of 87% for inpatient hallway beds. Reasons cited for this preference included better privacy, staff availability, safety, rest, quiet, and treatment [8]. Movement of patients to hallway beds and boosting discharges before noon are sometimes thought of as the “external factor,” as they involve practice outside of the ED, namely inpatient floors. There are of course a number of measures that can be undertaken to increase throughput in the ED that can be considered “internal factors.” These include streamlined and simplified triage, point-of-care laboratory testing for diagnostics that are a bottleneck to the next step, and care pathways, to name a few. The first step of solving any problem, acknowledging it, is well past. We know that patient volumes of EDs are going to increase at an exponential rate, as suggested by the CDC in 2005 [9], Our objectives should be to enable efficient care to everyone at all times. We have potential allies in technology, streamlined business management strategies, including Lean for process evolution and Six Sigma, and collaborative alliances between hospitals to develop creative solutions to the problem of ED over-crowding. In today’s evolving healthcare scenario, EDs are not only a point of care for the acutely ill, but also have a role as a safety net to provide healthcare to people regardless of their insurance status or ability to pay, as dictated by laws like the Emergency Medical Treatment and Labor Act (EMTALA) in the US. It is of paramount importance that a serious and unified approach to ED over-crowding be taken, at all levels. This would represent a major step towards establishing a culture of patient safety and satisfaction in the ED.

From the experiment using an underwater bell to determine the speed of sound by J. D. Colladon in 1826, the generation of ultrasound waves by Francis Galton in 1876, the discovery of the piezoelectric effect by Pierre Curie in 1880, the development of sonar systems within a month of the sinking of the Titanic in 1912, to its early conception as the “hydrophone” by Paul Langevin and Constantin Chilowsky in 1915 and the development of medical ultrasonic systems since 1940, ultrasound is a technology that has now reached the “tipping point” and is being rapidly assimilated into multiple medical specialties beyond radiology. Emergency medicine (EM) has witnessed the addition of ultrasonography to the arsenal of emergency care. The history of ultrasound in EM, spanning 15 years, can be traced back to the publication of the first emergency ultrasound curriculum by Mateer et al. in 1994. Increasing portability and the ease of training has fueled its rapid ascent to the front lines of EM practice to improve patient care. Since the publication of the first formal policy statement for the use of ultrasound in EM by the American College of Emergency Physicians in 2001 [1], the concept of emergency screening ultrasound has gained favor with most of the residency training programs in the USA and is now an element of the core curriculum required by the Residency Review Committee for Emergency Medicine [2]. It was also recently incorporated in the guidelines of the College of Emergency Medicine, UK in 2006 [3]. A large body of research studies has provided strong evidence that with appropriate training emergency ultrasound done by emergency physicians can safely aid time-critical decisions and procedures in the emergency department (ED). The gamut of emergency ultrasound has gone from FAST (focused sonography for trauma) to comprehensive advanced scans and procedural guidance such as scans for shortness of breath, increased intracranial pressure (ICP), and central line placement. The ability of ultrasound to be incorporated in emergency medical services (EMS), telemedicine, and medical student education has mushroomed interest in the field. We are pleased to feature ultrasound in this issue of the International Journal of Emergency Medicine. The first of the ultrasound articles in this issue is a review on its use in EMS [4]. Not only does it highlight the current status of its use in Europe and the USA, but it also provides insight into the training programs, point of care use in resuscitation and trauma by EMS, and the potential application in mass casualty incident triage. There is still a long way to go as far as credentialing EMS personnel is concerned, because of the known learning curve in ultrasound applications. The growth of ultrasound in EM is twofold—first is the development and validation of new point of care applications aptly demonstrated in the brief research report on “Ultrasound as an aid to reduction of pediatric forearm fractures” [5]. This application of ultrasound helps visually demonstrate successful reductions. Although the study did not demonstrate a decrease in the treatment time or radiological exposure, further validation will likely do so. The novelty of ultrasound applications has only multiplied with time, but the result remains the same: success. The second prong of growth is the promulgation of basic training in developing systems of care as is exemplified in the study on “Introduction of a portable ultrasound unit into the health services of the Lugufu refugee camp, Kigoma District, Tanzania” [6]. The study demonstrates the feasibility and the ease with which ultrasound can be adapted to use in complex, underdeveloped care systems to enhance patient care and satisfaction. This article goes a step further to demonstrate that the initial expenditure for procuring the equipment is greatly offset by the time gain and the decreased stress on ancillary medical departments in developing systems. The socioeconomic pressures of health care in all economies ranging from developed, developing and the underdeveloped will benefit from this boom of ultrasound in EM. But at the same time we must be wary that ultrasound in EM does not become a victim of its own success. Appropriate training and credentialing is the need of the hour. The spiraling growth of ultrasound in EM has only one foreseen outcome, a future made colorful by black and white.

The past decade has witnessed the emergence and substantial growth of the specialty of emergency medicine as a truly international medical discipline that refuses to be restrained by barriers of language, culture or geography. While the formal recognition of the specialty of emergency medicine is relatively young in most countries and nonexistent in some, the provision of emergency care is as ancient as the art of medicine itself. Descriptions of emergency care are documented in the Ebers Papyrus of ancient Egypt (2600 BC) [1], in the ancient teachings of traditional Chinese [2] and Ayurvedic [3] medicine and in the earliest writings of western medical care [4]. The growth of emergency medicine globally has been spurred by a number of factors, including population growth, urbanization, globalization and increasing expectations of patients around the world who are aware of what can be done to provide for emergency care [5]. The international growth of emergency medicine has resulted in an expanding presence of emergency medicine and related topics in scientific journals. In addition, the US has recently seen substantial growth in the number of international emergency medicine fellowship programs [6], and the international section and interest groups of emergency medicine societies are typically among the largest and most active in these organizations. A recent review of international emergency medicine from 2006 noted that one of the greatest obstacles to continue the growth of international emergency medicine remains the lack of high-quality, consolidated, and easily accessible evidence-based literature in this field [7]. This review of the literature from 2006 identified 25 international emergency medicine research articles. These articles include broad categories that cover the areas of evaluating EMS and trauma care services in both developed and developing countries, the global spread of emergency medicine and ways to improve emergency care for special populations within a developing country. A large proportion of international emergency medicine studies examines the methods for evaluating morbidity and mortality and improving the delivery of humanitarian aid during disasters and conflicts. The field of international emergency medicine has several major components that have been identified. These include (1) relief and disaster medicine, (2) prehospital care systems and infrastructure, (3) health-care policy at the national, regional and local levels, (4) clinical emergency medicine including the provision of emergency care in settings of limited resources, (5) emergency medicine research and (6) emergency medicine education. All of these domains have enjoyed development and continue to grow robustly.

When writing or reading articles, one should be aware whether the statistical tests performed were appropriate for the type of data collected and used, thereby avoiding misleading conclusions. The goal of all statistical tests is to determine whether two (or more) variables are associated with one another or independent from each other at the population level. In this issue of IJEM, our Clinical Research Capsule reviews the most common tests used in published literature and some of the pitfalls associated with their use. This article is intended for non-statisticians. One of the first things to keep in mind is the type of data and outcomes the author wants to measure and correlate. In order to do this, one must define the variables of the study. Are we looking at a continuous variable, one that can be quantified on an infinite scale, such as temperature or age, or is it a categorical variable, one that has to be grouped in classes. Categorical variables can be nominal or ordinal. Nominal variables are data that can be counted, but not ordered or measured. Nominal data can be further broken down into dichotomous (e.g., dead or alive) or have several categories (e.g., blood type). Ordinal data are numerical values that have a natural order and thus can be ranked and ordered. However, the distance between two values on an ordinal scale may not represent an equal degree of difference. For example, the modified Rankin score is a measure of outcome after stroke where a value of 0 is no functional deficit, and a value of 6 is dead. The difference between 0 and 1 is slight; however, the difference between a 2 and a 3 is very significant, as it distinguishes being functionally independent (2) versus dependent (3). Other examples of ordinal variables include birth order and pain severity scales. The second important thing to keep in mind is how the results are distributed. Do they follow a “bell curve” (also called a Gaussian distribution), similar to biological phenomena and exam grading techniques, or do the results tend to cluster resulting in a skewed distribution? (Fig. 1). With normally distributed data, mean and SD are reported. For skewed data, median and interquartile ranges are reported. Fig. 1 Left panel demonstrates a normal distribution of mean arterial pressure (MAP) in patients with acute ischemic stroke; right panel demonstrate skewed distribution of door to computed tomography time in patients with acute ischemic stroke Sometimes, it may be desirable to “normalize” skewed data. This is known as transformation. When data are skewed, the commonly applied transformations are 1/x, log(x), and sqrt(x), exponentiating, squaring, or cubing x, where the x’s are the data values. Also to be considered is whether the data are matched—meaning are sample subjects or data points related to one another, or are they independent? Once these three questions are answered, one is able to choose to the appropriate statistical test and, thus, decipher if an inappropriate test is used. For two dichotomous or binary variables, one will be able to build a 2 × 2 table. If the data follow a normal distribution, the most common test will be Chi-square test. It is used to compare the proportion of subjects in two groups, and verify the independence of each other. For example, if a study about a certain treatment obtains data that shows that it reduces mortality more than placebo for a given disease, one would like to know if the results are true or merely a coincidence. Therefore, we perform a Chi-square test and obtain the p value. One limitation of the Chi-square testing is that its distribution breaks down as the frequencies decrease. If in one of the cells of your table there are five or less observations, the data is considered skewed. In this case, you need to use Fisher’s exact test, specifically designed for small samples. For two continuous variables (e.g., respiratory rate versus age), one can use linear regression or correlation. Linear regression allows us to predict the outcome for a particular value of the predictor. Correlation help us measure the association and direction between the variables. In cases where the outcome or dependent variable (Y axis) is continuous (e.g., high blood pressure) and the independent variable (X axis) is binary (e.g., smoking yes/no); the distribution of the dependent variable will guide one in using (1) parametric tests [t test and analysis of variance (ANOVA)] for normally distributed data, or (2) nonparametric tests (Wilcoxon/Kruskal–Wallis or rank sum tests) for skewed data (Table 1). Table 1 Statistical test suggested In cases where the outcome or dependent variable (Y axis) is binary and the independent variable (X axis) is continuous, one should use logistic regression analyses. When the data is matched (e.g., before and after measurement of a variable in the same patient), the appropriate test would be the McNemar test. There are many sources of errors when selecting a statistical test. The first involves sources of bias. These are conditions or circumstances which affect the external validity of statistical results. The second are errors in methodology, which can lead to inaccurate or invalid results. The third are interpretation of results or how statistical results are applied to real world issues. Common pitfalls: Reporting the skewed data with mean and SD. Normally distributed data should be reported with mean (average) and SD or confidence intervals, and skewed data should be reported with median and interquartile ranges. The study’s overall statistical analyses were not performed to reject the null hypothesis. If the investigator constructs a loose protocol and allows the experimenters to vary how they conduct the experimental procedures or interviews with different subjects, it is likely that the results of the experiment will be misleading. The decision not to use the data was made after inspection of the results and without a predetermined rationale. After an overall analysis had failed to reject the null hypothesis, the investigators perform a large number of new statistical tests on the data. Do not take account of changing levels of significance when many statistical tests were performed on a single set of data (for example, perform 20 comparisons with one set of data) Data omission—i.e., all the data in the analyses are not included (deleting patients with “inconvenient” results) Conclusions cannot be derived form the results of the study Reporting only p values. The mean, median, SD, confidence interval, relative risk, odds ratio, etc. should all be reported, to allow the reader to critique for him/herself the validity of the results. Look at magnitudes rather than p values. Causal inference. Observational studies are very limited in their ability to show causal relationships. We will require a multifaceted approach to the research use of chronologically structured designs (placing variables in the roles of antecedents and outcomes) and ability of replication, to come to any conclusions regarding causality. Precision and accuracy. Precision refers to how finely an estimate is specified; whereas accuracy refers to how close an estimate is to the true value. Estimates can be precise without being accurate. There seems to be an erroneous notion that you can prove anything with statistics. However, this is only true if you use them improperly. Many times the data is overlooked, or the statistical test is not correctly selected. Always keep in mind that the simpler the experiment, the better will be its execution, and the more likely will one be able to see the “real truth” and what the results actually mean.

April 3rd marked the debut of the International Journal of Emergency Medicine’s first issue and the first meeting of the Editorial Board. Several excellent ideas for advancing the mission of international emergency medicine arose. Of these, one recurrent theme was that of bringing to publication important work occurring in developing countries. While this is clearly one of the goals of the International Journal of Emergency Medicine, simply welcoming such contributions may not be adequate. Several barriers may preclude a manuscript from even being submitted, such as language barriers, inexperience with scientific writing or lack of guidance for how to develop a given manuscript. The importance of mentorship in a successful academic career has been documented [1]. With these concerns in mind, the Editorial Board of the International Journal of Emergency Medicine has decided to institute three new journal features: An academic mentorship programme for authors wishing to contribute their work to the International Journal of Emergency Medicine Standardized reporting guidelines for various study designs and A new journal feature entitled “clinical research capsule”. The International Journal of Emergency Medicine academic mentorship programme is proposed to function as follows: Authors interested in the programme will fill out an electronic request that will be available on the website. The form will ask for information regarding the subject of the manuscript, the stage that it is in and the type of assistance required. Upon receipt of the form, the proposal will be evaluated. If accepted, a member of the Editorial Board will be assigned to the primary author, who will work with him or her to produce a high quality manuscript suitable for publication in the International Journal of Emergency Medicine. Standardized reporting guidelines: The International Journal of Emergency Medicine encourages reporting of research studies according to the following standardized guidelines referred to in the Uniform Requirements for Manuscripts Submitted to Biomedical Journals by the International Committee of Medical Journal Editors. These include: STROBE [2] (STrengthening the Reporting of OBservational studies in Epidemiology) statement—checklist of items that should be included in reports of cohort studies STARD [3] statement–checklist of items that should be included in reports of diagnostic studies MOOSE [4] statement–checklist of items that should be included in reports of meta-analyses CONSORT [5] (Consolidated Standards of Reporting Trials) statement—checklist of items that should be included in reports of randomized clinical trials

Objective To assess relationships between blood pressure hemodynamic measures and outcomes after acute ischemic stroke, including stroke severity, disability and death. Methods The study cohort consisted of 189 patients who presented to our emergency department with ischemic stroke of less than 24 hours onset who had hemodynamic parameters recorded and available for review. Blood pressure (BP) was non-invasively measured at 5 minute intervals for the length of the patient's emergency department stay. Systolic BP (sBP) and diastolic BP (dBP) were measured for each patient and a differential (the maximum minus the minimum BP) calculated. Three outcomes were studied: stroke severity, disability at hospital discharge, and death at 90 days. Statistical tests used included Spearman correlations (for stroke severity), Wilcoxon test (for disability) and Cox models (for death). Results Larger differentials of either dBP (p = 0.003) or sBP (p < 0.001) were significantly associated with more severe strokes. A greater dBP (p = 0.019) or sBP (p = 0.036) differential was associated with a significantly worse functional outcome at hospital discharge. Those patients with larger differentials of either dBP (p = 0.008) or sBP (0.007) were also significantly more likely to be dead at 90 days, independently of the basal BP. Conclusion A large differential in either systolic or diastolic blood pressure within 24 hours of symptom onset in acute ischemic stroke appears to be associated with more severe strokes, worse functional outcome and early death

Background To characterize the patterns of presentation of adults with head injury to the Emergency Department. Methods This is a cohort study that sought to collect injury and outcome variables with the goal of characterizing the very early natural history of traumatic brain injury in adults. This IRB-approved project was conducted in collaboration with our Institution’s Center for Translational Science Institute. Data were entered in REDCap, a secure database. Statistical analyses were performed using JMP 10.0 pro for Windows. Results The cohort consisted of 2,394 adults, with 40% being women and 79% Caucasian. The most common mechanism was fall (47%) followed by motor vehicle collision (MVC) (36%). Patients sustaining an MVC were significantly younger than those whose head injury was secondary to a fall (P < 0.0001). Ninety-one percent had CT imaging; hemorrhage was significantly more likely with worse severity as measured by the Glasgow Coma Score (chi-square, P < 0.0001). Forty-four percent were admitted to the hospital, with half requiring ICU admission. In-hospital death was observed in 5.4%, while neurosurgical intervention was required in 8%. For all outcomes, worse TBI severity per GCS was significantly associated with worse outcomes (logistic regression, P < 0.0001, adjusted for age). Conclusion These cohort data highlight the burden of TBI in the Emergency Department and provide important demographic trends for further research.